I've noticed two dtype promotion behaviors that are surprising to me.
I'm hoping to get people's input as to whether I should file bug tickets
on them.
First weirdness:
When you do an operation between a float32 Numpy array and a python or
Numpy float64, the result is a float32 array, not a float64 array:
>>> import numpy as N
>>> vec32 = N.ones(2, N.float32)
>>> vec32
array([ 1., 1.], dtype=float32)
>>> result = vec32 - 1.0
>>> result.dtype
dtype('float32')
>>> result = vec32 - N.float64(1.0)
>>> result.dtype
dtype('float32')
This is of course not the case if the float64 is replaced with a
1-dimensional numpy array:
>>> result = vec32 - N.array([1.0], N.float64)
>>> result.dtype
dtype('float64')
Second weirdness:
Type promotion doesn't happen if you replace the 1-d, 1-element array
with a 0-d array:
>>> result = vec32 - N.array(1.0, N.float64)
>>> result.dtype
dtype('float32')
The second weirdness in particular strikes me as inconsistent when
compared to operations between vec32 and the 1-d array.
I didn't see these listed on the trac site as bugs, but then again a
bunch of tickets seem to have been removed/fixed recently. Sorry if
these have been mentioned, filed, and fixed already. Otherwise, should I
file a ticket on the above behaviors?
-- Matt